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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

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        About MultiQC

        This report was generated using MultiQC, version 1.21

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        Report generated on 2025-02-19, 00:13 CET based on data in: /vulpes/proj/ngis/ngi2016004/private/strategic_proj/SR_23_02_Element_vs_Illumina/analysis/differential_coverage


        General Statistics

        Showing 36/36 rows and 6/18 columns.
        Sample Name≥ 30XMedianMean Cov.Median CoverageBases ≥ 30XReads mapped
        aviti_hq_KMS12BM
        0.4%
        9.0X
        9.4
        aviti_hq_KMS12BM.collect_wgs_metrics
        50.0X
        81%
        aviti_hq_KMS12BM.downsampled
        217.3M
        aviti_hq_MM1S
        0.1%
        10.0X
        9.3
        aviti_hq_MM1S.collect_wgs_metrics
        50.0X
        86%
        aviti_hq_MM1S.downsampled
        218.0M
        aviti_hq_OPM2
        0.3%
        9.0X
        9.4
        aviti_hq_OPM2.collect_wgs_metrics
        48.0X
        87%
        aviti_hq_OPM2.downsampled
        217.8M
        aviti_hq_REH
        0.1%
        10.0X
        9.4
        aviti_hq_REH.collect_wgs_metrics
        45.0X
        87%
        aviti_hq_REH.downsampled
        217.6M
        aviti_ngi_KMS12BM
        0.4%
        9.0X
        9.3
        aviti_ngi_KMS12BM.collect_wgs_metrics
        17.0X
        7%
        aviti_ngi_KMS12BM.downsampled
        222.9M
        aviti_ngi_MM1S
        0.1%
        9.0X
        9.3
        aviti_ngi_MM1S.collect_wgs_metrics
        17.0X
        4%
        aviti_ngi_MM1S.downsampled
        223.6M
        aviti_ngi_OPM2
        0.3%
        9.0X
        9.3
        aviti_ngi_OPM2.collect_wgs_metrics
        19.0X
        9%
        aviti_ngi_OPM2.downsampled
        223.7M
        aviti_ngi_REH
        0.1%
        10.0X
        9.3
        aviti_ngi_REH.collect_wgs_metrics
        15.0X
        1%
        aviti_ngi_REH.downsampled
        222.6M
        xplus_sns_KMS12BM
        0.4%
        9.0X
        9.6
        xplus_sns_KMS12BM.collect_wgs_metrics
        50.0X
        82%
        xplus_sns_KMS12BM.downsampled
        285.0M
        xplus_sns_MM1S
        0.1%
        10.0X
        9.5
        xplus_sns_MM1S.collect_wgs_metrics
        50.0X
        87%
        xplus_sns_MM1S.downsampled
        289.7M
        xplus_sns_OPM2
        0.4%
        10.0X
        9.5
        xplus_sns_OPM2.collect_wgs_metrics
        55.0X
        92%
        xplus_sns_OPM2.downsampled
        289.7M
        xplus_sns_REH
        0.1%
        10.0X
        9.6
        xplus_sns_REH.collect_wgs_metrics
        46.0X
        89%
        xplus_sns_REH.downsampled
        295.6M

        mosdepth

        mosdepth performs fast BAM/CRAM depth calculation for WGS, exome, or targeted sequencing.DOI: 10.1093/bioinformatics/btx699.

        Cumulative coverage distribution

        Proportion of bases in the reference genome with, at least, a given depth of coverage. Calculated across the entire genome length

        For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position, while the breadth of coverage is the fraction of the reference sequence to which reads have been mapped with at least a given depth of coverage (Sims et al. 2014).

        Defining coverage breadth in terms of coverage depth is useful, because sequencing experiments typically require a specific minimum depth of coverage over the region of interest (Sims et al. 2014), so the extent of the reference sequence that is amenable to analysis is constrained to lie within regions that have sufficient depth. With inadequate sequencing breadth, it can be difficult to distinguish the absence of a biological feature (such as a gene) from a lack of data (Green 2007).

        For increasing coverage depths (1×, 2×, …, N×), coverage breadth is calculated as the percentage of the reference sequence that is covered by at least that number of reads, then plots coverage breadth (y-axis) against coverage depth (x-axis). This plot shows the relationship between sequencing depth and breadth for each read dataset, which can be used to gauge, for example, the likely effect of a minimum depth filter on the fraction of a genome available for analysis.

        Created with MultiQC

        Coverage distribution

        Proportion of bases in the reference genome with a given depth of coverage. Calculated across the entire genome length

        For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position (Sims et al. 2014).

        Bases of a reference sequence (y-axis) are groupped by their depth of coverage (0×, 1×, …, N×) (x-axis). This plot shows the frequency of coverage depths relative to the reference sequence for each read dataset, which provides an indirect measure of the level and variation of coverage depth in the corresponding sequenced sample.

        If reads are randomly distributed across the reference sequence, this plot should resemble a Poisson distribution (Lander & Waterman 1988), with a peak indicating approximate depth of coverage, and more uniform coverage depth being reflected in a narrower spread. The optimal level of coverage depth depends on the aims of the experiment, though it should at minimum be sufficiently high to adequately address the biological question; greater uniformity of coverage is generally desirable, because it increases breadth of coverage for a given depth of coverage, allowing equivalent results to be achieved at a lower sequencing depth (Sampson et al. 2011; Sims et al. 2014). However, it is difficult to achieve uniform coverage depth in practice, due to biases introduced during sample preparation (van Dijk et al. 2014), sequencing (Ross et al. 2013) and read mapping (Sims et al. 2014).

        This plot may include a small peak for regions of the reference sequence with zero depth of coverage. Such regions may be absent from the given sample (due to a deletion or structural rearrangement), present in the sample but not successfully sequenced (due to bias in sequencing or preparation), or sequenced but not successfully mapped to the reference (due to the choice of mapping algorithm, the presence of repeat sequences, or mismatches caused by variants or sequencing errors). Related factors cause most datasets to contain some unmapped reads (Sims et al. 2014).

        Created with MultiQC

        Average coverage per contig

        Average coverage per contig or chromosome

        Created with MultiQC

        XY coverage

        Created with MultiQC

        Picard

        Picard is a set of Java command line tools for manipulating high-throughput sequencing data.

        GC Coverage Bias

        This plot shows bias in coverage across regions of the genome with varying GC content. A perfect library would be a flat line at y = 1.

        Created with MultiQC

        WGS Coverage

        The number of bases in the genome territory for each fold coverage. Note that final 1% of data is hidden to prevent very long tails.

        Created with MultiQC

        WGS Filtered Bases

        For more information about the filtered categories, see the Picard documentation.

        Created with MultiQC

        Samtools

        Samtools is a suite of programs for interacting with high-throughput sequencing data.DOI: 10.1093/bioinformatics/btp352.

        Flagstat

        This module parses the output from samtools flagstat. All numbers in millions.

        Created with MultiQC